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Looking at the results, angle sensors and angular velocity sensors were effective in determining road surface shapes and properties, respectively.
Our experiments showed that the adoption of reinforcement learning for determining road pricing policies is a promising approach, even with limitations in the driver agent and link manager models.
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The method thus allows flood hazards to be estimated and also provides insights into the relative roles of landscape characteristics in determining road-related flood hazards.
Cities have even less resources, so they tend to rely on reporting by citizens and visual examination by inspectors to determine road maintenance.
This agrees with the fact that ADT is among the most effective parameters to determine road geometry and therefore, it is directly related to number of accidents.
In this paper we used archived cadastral maps to determine road age and examine potential variability in roadside vegetation structures in a typical rural landscape in south-eastern Australia.
This indicates that economic growth may determine road safety in a region.
Positioning data will be used to determine road type and other environmental variables.
First module is detection of road network using different morphological direction filter, and second module is extraction of road intersection to determine road orientation.
We model traffic as a multiagent system in which link manager agents employ a reinforcement learning scheme to determine road pricing policies in a road network.
First, we propose a road traffic collecting protocol where a source node disseminates a query message into the network to determine road traffic information along predefined routes towards the destination location.
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